This proposal focuses on the development of statistical and computational methods for the analysis of data coming from high-throughput functional genomics technology such as microarrays. The research involves the extraction of meaningful relative expression levels from such experiments, and the subsequent identification of DNA sequence features such as promoters and transcription factor binding sites shared by genes identified from the arrays. This research involves the close collaboration of computational scientists and laboratory scientists. The projects involve studies of aging (in nematode, fruit fly and man), RNA structure prediction, identification of features of DNA replication in yeast, sensitivity to radiation therapy in human cancer patients, hybrid vigor in the development of oysters, and the evolution of E. coll. The common themes are the acquisition, analysis and interpretation of large amounts of data generated through the use of various microarray technologies. New approaches to research and training are needed for studying problems in genomics and molecular biology. The traditional one-person or student-advisor study is unlikely to be successful. Instead a multidisciplinary approach must be devised, with mathematical scientists involved with experimentalists to understand the motivations for the experiments and the limitations of the studies. Post-doctoral and graduate students from mathematics and statistics must learn the skills of cross-disciplinary communication and collaboration. This research program is specifically structured to provide practical and continuing training in this process.